Image-based Modelling and Simulation

Imaging modalities are increasingly used to monitor or test the state of materials and processes in a non-destructive manner. However, the step from imaging to computational simulation and analysis requires labour intensive segmentation and meshing process each with their own uncertainties. Furthermore, there is uncertainty in the imaging data itself, which can have a large effect on the simulation results. We propose to focus on the development of a framework to ease the gap between image and model. Tools will be developed that identify/extract image characteristics/statistics (e.g. material properties, boundary conditions, geometry) using machine learning techniques, which will then translate into a range of representative computational models that explore the causal effects of the imaging uncertainties through HPC. Envisaged projects will use microCT data on material microstructures (static) on and ultrasound images on flows (dynamic) to create and test the framework. The resulting framework should eliminate some of the uncertainties typical incurred in image-based modelling and simulation pipelines, whilst easing the integration of imaging modalities in diagnostics by simplifying the processes and involvement.